1. Field of the Invention
This invention relates to a method and apparatus for detecting a prospective abnormal pattern, typically a tumor pattern, which is embedded in a radiation image.
2. Description of the Prior Art
Image processing, such as gradation processing or frequency processing, has heretofore been carried out on an image signal, which represents an image and has been obtained with one of various image obtaining methods, such that a visible image having good image quality can be reproduced and used as an effective tool in, particularly, the accurate and efficient diagnosis of an illness. Particularly, in the field of medical images, such as radiation images of human bodies serving as objects, it is necessary for specialists, such as doctors, to make an accurate diagnosis of an illness or an injury of the patient in accordance with the obtained image. Therefore, it is essential to carry out the image processing in order that a visible image having good image quality can be reproduced and used as an effective tool in the accurate and efficient diagnosis of an illness.
In such image processing, the processing is often carried out on the entire area of the image. Alternatively, in cases where the purpose of examination or diagnosis is clear to a certain extent, the emphasis processing is often carried out selectively on a desired image portion, which is adapted to the purpose of examination or diagnosis.
Ordinarily, when an image portion to be processed is to be selected, the person, who views the radiation image, views the original image before being processed and manually selects the image portion to be processed. However, there is the risk that the selected image portion or the specified image range will vary, depending upon the level of the experience or the image understanding capability of the person, who views the radiation image, and the selection cannot be carried out objectively.
For example, in cases where a radiation image has been recorded for the examination of breast cancer, it is necessary to find a tumor pattern, which is one of features of a cancerous portion, from the radiation image. However, the range of the tumor pattern cannot always be specified accurately. Therefore, there is a strong demand for techniques for accurately detecting an abnormal pattern, such as a tumor pattern, without depending upon the skill of the person, who views the radiation image.
As one of the techniques for satisfying the aforesaid demand, iris filter processing (hereinbelow often referred to as the operation of the iris filter) has heretofore been proposed. [Reference should be made to "Detection of Tumor Patterns in DR Images (Iris Filter)," Obata, et al., Collected Papers of The Institute of Electronics and Communication Engineers of Japan, D-II, Vol. J75-D-II, No.3, pp. 663-670, March 1992.] The iris filter processing has been studied as a technique efficient for detecting, particularly, a tumor pattern, which is one of characteristic forms of mammary cancers. However, the image to be processed with the iris filter is not limited to the tumor pattern in a mammogram, and the iris filter processing is applicable to any kind of image portion having the characteristics such that the gradients of the image signal (the image density, or the like) representing the image are centralized.
How the processing for detecting a prospective abnormal pattern with the iris filter is carried out will be described hereinbelow by taking the processing for the detection of the tumor pattern as an example.
It has been known that, for example, in a radiation image recorded on X-ray film (i.e., an image yielding an image signal of a high signal level for a high image density), the image density values of a tumor pattern are slightly smaller than the image density values of the surrounding image areas. The image density values of the tumor pattern are distributed such that the image density value becomes smaller from the periphery of an approximately circular tumor pattern toward the center point of the tumor pattern. Thus the distribution of the image density values of the tumor pattern has gradients of the image density values. Therefore, in the tumor pattern, the gradients of the image density values can be found in local areas, and the gradient lines (i.e., gradient vectors) centralize in the directions heading toward the center point of the tumor pattern.
The iris filter calculates the gradients of image signal values, which are represented by the image density values, as gradient vectors and feeds out the information representing the degree of centralization of the gradient vectors. With the iris filter processing. a tumor pattern is detected in accordance with the degree of centralization of the gradient vectors.
Specifically, by way of example, as illustrated in FIG. 7A, a tumor pattern P1 may be embedded in a mammogram P. As illustrated in FIG. 7B, the gradient vector at an arbitrary picture element in the tumor pattern P1 is directed to the vicinity of the center point of the tumor pattern P1. On the other hand, as illustrated in FIG. 7C, in an elongated pattern P2, such as a blood vessel pattern or a mammary gland pattern, gradient vectors do not centralize upon a specific point. Therefore, the distributions of the directions of the gradient vectors in local areas may be evaluated, and a region, in which the gradient vectors centralize upon a specific point, may be detected. The thus detected region may be taken as a prospective tumor pattern, which is considered as being a tumor pattern. As illustrated in FIG. 7D, in a pattern P3, in which elongated patterns, such as mammary gland patterns, intersect each other, gradient vectors are liable to centralize upon a specific point. Therefore, the pattern P3 may be detected as a false positive. The processing with the iris filter is based on such fundamental concept. Steps of algorithms of the iris filter will be described hereinbelow.
(Step 1) Calculation of Gradient Vectors
For each picture element j among all of the picture elements constituting a given image, the direction .theta. of the gradient vector of the image signal representing the image is calculated with Formula (1) shown below. ##EQU1##
As illustrated in FIG. 8, f.sub.1 through f.sub.16 in Formula (1) represent the picture element values (i.e., the image signal values) corresponding to the picture elements located at the peripheral areas of a mask, which has a size of five picture elements (located along the column direction of the picture element array) x five picture elements (located along the row direction of the picture element array) and which has its center at the picture element j.
(Step 2) Calculation of the Degree of Centralization of Gradient Vectors
Thereafter, for each picture element among all of the picture elements constituting the given image, the picture element is taken as a picture element of interest, and the degree of centralization C of the gradient vectors with respect to the picture element of interest is calculated with Formula (2) shown below. ##EQU2##
As illustrated in FIG. 9, in Formula (2), N represents the number of the picture elements located in the region inside of a circle, which has its center at the picture element of interest and has a radius R, and .theta.j represents the angle made between the straight line, which connects the picture element of interest and each picture element j located in the circle, and the gradient vector at the picture element j, which gradient vector has been calculated with Formula (1). Therefore, in cases where the directions of the gradient vectors of the respective picture elements j centralize upon the picture element of interest, the degree of centralization C represented by Formula (2) takes a large value.
The gradient vector of each picture element j, which is located in the vicinity of a tumor pattern, is directed approximately to the center portion of the tumor pattern regardless of the level of the contrast of the tumor pattern. Therefore, it can be regarded that the picture element of interest associated with the degree of centralization C, which takes a large value, is the picture element located at the center portion of the tumor pattern. On the other hand, in a linear pattern, such as a blood vessel pattern, the directions of the gradient vectors are biased to a certain direction, and therefore the value of the degree of centralization C is small. Accordingly, a tumor pattern can be detected by taking each of all picture elements, which constitute the image, as the picture element of interest, calculating the value of the degree of centralization C with respect to the picture element of interest, and rating whether the value of the degree of centralization C is or is not larger than a predetermined threshold value. Specifically, the processing with the iris filter has the features over an ordinary difference filter in that the processing with the iris filter is not apt to be adversely affected by blood vessel patterns, mammary gland patterns, or the like, and can efficiently detect tumor patterns.
In actual processing, such that the detection performance unaffected by the sizes and shapes of tumor patterns may be achieved, it is contrived to adaptively change the size and the shape of the filter. FIG. 10 shows an example of the filter. The filter is different from the filter shown in FIG. 9. With the filter of FIG. 10, the degree of centralization is rated only with the picture elements, which are located along radial lines extending radially from a picture element of interest in M kinds of directions at 2.pi./M degree intervals. (In FIG. 10, by way of example, 32 directions at 11.25 degree intervals are shown.)
In cases where the picture element of interest has the coordinates (k, 1), the coordinates ([x], [y]) of the picture element, which is located along an i'th radial line and is the n'th picture element as counted from the picture element of interest, are given by Formulas (3) and (4) shown below. EQU x=k+n cos {2.pi.(i-1)/M} (3) EQU y=1+n sin {2.pi.(i-1)/M} (4)
wherein [x] represents the maximum integer, which does not exceedx, and [y] represents themaximum integer, which does not exceed y.
Also, for each of the radial lines, the output value obtained for the picture elements ranging from a certain picture element to a picture element, which is located along the radial line and at which the maximum degree of centralization is obtained, is taken as the degree of centralization Cimax with respect to the direction of the radial line. The mean value of the degrees of centralization Cimax, which have been obtained for all of the radial lines, is then calculated. The mean value of the degrees of centralization Cimax having thus been calculated is taken as the degree of centralization C of the gradient vector group with respect to the picture element of interest.
Specifically, the degree of centralization Ci(n), which is obtained for the picture elements ranging from the picture element of interest to the n'th picture element located along the i'th radial line, is calculated with Formula (5) shown below. ##EQU3##
wherein Rmin and Rmax respectively represent the minimum value and the maximum value having been set for the radius of the tumor pattern, which is to be detected.
Specifically, with Formula (5), the degree of centralization Ci(n) is calculated with respect to all of the picture elements, which are located along each of the radial lines and fall within the range from the picture element of interest, that is located on each radial line, to apicture element, that is located at a length of distance falling within the range from a length of distance corresponding to the minimum value Rmin having been set for the radius of the tumor pattern to be detected to a length of distance corresponding to the maximum value Rmax.
Thereaf ter, the degree of centralization C of the gradient vector group is calculated with Formulas (6) and (7) shown below. ##EQU4##
The value of Cimax of Formula (6) represents the maximum value of the degree of centralization Ci(n) obtained for each of the radial lines with Formula (5). Therefore, the region from the picture element of interest to the picture element associated with the degree of centralization Ci(n), which takes the maximum value, may be considered as being the region of the prospective tumor pattern along the direction of the radial line.
The calculation with Formula (6) is made for all of the radial lines, and the regions of the prospective tumor pattern on all of the radial lines are thereby detected. The regions of the prospective tumor pattern on the adjacent radial lines are then connected by a straight line or a non-linear curve. In this manner, it is possible to specify the shape of the peripheral edge of the region, which may be regarded as the prospective tumor pattern.
Thereafter, with Formula (7), the mean value of the maximum values Cimax of the degrees of centralization within the aforesaid regions, which maximum values Cimax have been given by Formula (6) for all directions of the radial lines, is calculated. In Formula (7), by way of example, the radial lines are set along 32 directions. The calculated mean value serves as an output value I of the iris filter processing. The output value is compared with a predetermined constant threshold value T, which is appropriate for making a judgment as to whether the detected pattern is or is not a prospective tumor pattern. In cases where I.gtoreq.T, it is judged that the region having its center at the picture element of interest is a prospective abnormal pattern (a prospective tumor pattern). In cases where I&lt;T, it is judged that the region having its center at the picture element of interest is not a prospective tumor pattern.
The size and the shape of the region, in which the degree of centralization C of the gradient vector group with Formula (7) is rated, change adaptively in accordance with the distribution of the gradient vectors. Such an adaptive change is similar to the manner, in which the iris of the human's eye expands or contracts in accordance with the brightness of the external field. Therefore, the aforesaid technique for detecting the region of the prospective tumor pattern by utilizing the degrees of centralization of the gradient vectors is referred to as the iris filter processing.
The calculation of the degree of centralization Ci(n) may be carried out by using Formula (5') shown below in lieu of Formula (5). ##EQU5##
Specifically, with Formula (5'), the degree of centralization Ci (n) is calculated with respect to all of the picture elements, which are located along each of the radial lines and fall within the range from a picture element, that is located at a length of distance corresponding to the minimum value Rmin having been set for the radius of the tumor pattern to be detected, the length of distance being taken from the picture element of interest located on each radial line, to a picture element, that is located at a length of distance falling within the range from the length of distance corresponding to the minimum value Rmin to a length of distance corresponding to the maximum value Rmax, the length of distance being taken from the picture element of interest located on each radial line.
By carrying out the steps described above, the iris filter can efficiently detect only the tumor pattern, which has a desired size, from a radiation image. Research has heretofore been carried out on the iris filter particularly for the purpose of detecting a cancerous portion from a mammogram.
Radiation images are often recorded such that a pattern, such as a mammary gland pattern, which is not an abnormal pattern, quantum noise, or the like, is superposed upon an abnormal pattern. In such cases, the direction of the image density gradient vector deviates from the center point of the abnormal pattern, and the degree of centralization of the image density gradient vector becomes lower than the degree of centralization in an image, in which a pattern other than an abnormal pattern, quantum noise, or the like, is not superposed upon the abnormal pattern. Therefore, the output value I of the iris filter processing becomes small, and there is the risk that the output value I becomes smaller than a predetermined threshold value and the abnormal pattern is not detected as the prospective abnormal pattern.
Also, the output value I of the iris filter processing takes a large value for an abnormal pattern, which has a contour shape close to a true circle. However, all abnormal patterns do not necessarily have a contour shape close to a true circle, and there are abnormal patterns having slightly uneven contour shapes. As for such a distorted abnormal pattern, the output value I of the iris filter processing becomes small. Therefore, as in the aforesaid cases where noise is superposed upon the abnormal pattern, there is the risk that the distorted abnormal pattern is not detected as the prospective abnormal pattern.
However, if the aforesaid threshold value is merely set to be small such that the prospective abnormal pattern, which is to be detected, can be detected, the problems will occur in that a false positive (FP), such as a nipple pattern, which is actually not the abnormal pattern, is detected as the prospective abnormal pattern. In such cases, considerable time and labor will be required for a person who views the radiation image, such as a medical doctor, to make a judgment.
FIG. 3A shows a radiation image (a negative image recorded on photographic film) P, in which a pattern P0 of the mamma serving as an object is embedded. By way of example, the iris filter processing may be carried out on the radiation image P. In such cases, as illustrated in FIG. 3B and 3C, an output value II is obtained for a tumor pattern PI, and an output value I2 (=0) is obtained for a mammary gland pattern P2. Also, an output value I3 is obtained for a pattern P3, at which a mammary gland pattern and a blood vessel pattern intersect each other, and an output value I4 is obtained for a nipple pattern P4.
For example, if quantum noise is superposed upon the tumor pattern P1, the output value I1 for the tumor pattern P1 will become small and approximately equal to the output value I4 for the nipple pattern P4. In such cases, if the threshold value is set to be a large value, T2, the tumor pattern P1 cannot be detected. If the threshold value is set to be a small value, T1, the tumor pattern P1 can be detected, but the nipple pattern P4, which is not a tumor pattern, will also be detected.